Reinforcement learning (RL) in continuous-valued state and input spaces relies on function
approximators.
Various types of numerical approximators have been used to represent the value function and policy mappings:
expansions with fixed or adaptive basis functions
[1]}, [2]}, regression trees
[3]}, local linear regression
[4]}, [5]}, and deep
neural networks
[6]}, [7]}, [8]}, [9]}, [10]}.
